The biological nervous system is a highly efficient and powerful organ for the processing of information. A feature of the biological nervous system is its capability of responding to a wide range of stimuli with an analog, rather than a binary, response. The biological nervous system is also capable of adapting to different conditions and may also be taught to learn to adapt to variable conditions.
Although biological prototypes may not be duplicated exactly by utilizing artificial neural systems and networks, it is desirable to provide neural systems and networks having similar characteristics, for example, an analog response which varies over a range of stimulus. It is also desirable to simulate with neural systems and networks, the adaptability of the biological nervous system to perform many different logic functions. An artificial neural system is defined as a dynamical system that can carry out useful information processing by utilizing state response to initial or continuous input. The most common structures in artificial neural systems are networks of processing elements or "neurons" that are interconnected via information channels. Each neuron can have multiple input signals, but generates only one output signal. The inputs to a neuron are generally copies of output signals from other neurons as well as inputs from outside the network. The behavior of neurons, the relationship between their inputs and outputs, are generally governed by first-order ordinary differential equations in the output signal variable.
By providing some or all of the neurons in a network with the capability to self-adjust, some of the coefficients in their governing differential equations, the network can be rendered adaptive. The idea of a self-adapting dynamical system that can modify its response to external force in response to experience is one of the central concepts of an artificial neural system. Such systems therefore have the processing capabilities for real-time high-performance pattern recognition. Different patterns may be recognized by adapting a neural logic system to perform different logic functions and thereby respond to significant features which characterize a certain pattern. As used herein, patterns may represent, for example, alphanumeric symbols; objects within a scene, such as military targets; blood cells; defects in manufactured items; speech patterns; spatial representations of lines; and the like.
Many previously developed pattern recognition systems utilize linear discriminant functions on a set of features of the input pattern which limits the performance of such systems. Neural network systems, on the other hand, are non-linear and as such, have the potential to provide better solutions. Such networks also adapt, or learn, the solution in an iterative manner and have the advantage of discovering features necessary for recognizing patterns in image data. Previously developed neural network systems, however, suffer from their slow ability to learn. A need has thus developed for a neural network system that can learn arbitrary associations and recognize patterns quickly.